3 papers
cs.AI2026
Make LLM Learn to Synthesize from Streaming Experiences through Feedback
Zhenlin Hu, Yan Wang, Zhen Bi +7
Large language models (LLMs) have been widely adopted for synthetic data generation, significantly reducing annotation costs. However, most existing studies treat synthesis as a se…
cs.AI2026
Logical Structure as Knowledge: Enhancing LLM Reasoning via Structured Logical Knowledge Density Estimation
Zhen Bi, Zhenlin Hu, Xueshu Chen +7
The reasoning capabilities of Large Language Models (LLMs) are increasingly attributed to training data quality rather than mere parameter scaling. However, existing data-centric p…
cs.LG2026
Thought Purity: A Defense Framework For Chain-of-Thought Attack
Zihao Xue, Zhen Bi, Long Ma +7
Large Reasoning Models (LRMs) leverage Chain-of-Thought (CoT) reasoning to solve complex tasks, but this explicit reasoning process introduces a critical vulnerability: adversarial…